Patient Selection For Clinical Trials Research Articles

Future for cardiogenic shock research.

To discuss future research themes and study design in cardiogenic shock. Cardiogenic shock research faces multiple challenges, hindering progress in understanding and treating this life-threatening condition. Cardiogenic shock's heterogeneous nature poses challenges in patient selection for clinical trials, potentially leading to variability in treatment responses and outcomes. Ethical considerations arise due to the acuity and severity of the condition, posing challenges in obtaining informed consent and conducting randomized controlled trials where time to treatment is pivotal. This review discusses research in this area focusing on the importance of phenotyping patients with cardiogenic shock, based on artificial intelligence, machine learning, and unravel new molecular mechanisms using proteomics and metabolomics. Further, the future research focus in mechanical circulatory support and targeting inflammation is reviewed. Finally, newer trial designs including adaptive platform trials are discussed.

Validation of a simple clinical tool for screening of acute lacunar stroke-A substudy of the WAKE-UP trial.

Lacunar stroke represents around a quarter of all ischemic strokes; however, their identification with computed tomography in the hyperacute setting is challenging. We aimed to validate a clinical score to identify lacunar stroke in the acute setting, independently, with data from the WAKE-UP trial using magnetic resonance imaging. We analyzed data from the WAKE-UP trial and extracted Oxfordshire Community Stroke Project (OCSP) classification. Lacunar score was defined by National Institutes of Health Stroke Scale (NIHSS) < 7 and OCSP lacunar syndrome. Assessment of lacunar infarct by two independent investigators was blinded to clinical data. We calculated sensitivity, specificity, negative and positive predictive value (NPV and PPV, respectively) of lacunar score. We included 503 patients in the analysis, mean (±SD) age 65.2 (±11.6) years, 325 (65%) males, median (IQR) NIHSS = 6 (4-9); 108 (22%) lacunar infarcts were identified on magnetic resonance (MR), patients fulfilling lacunar score criteria were 120 (24%), of which 47 (44%) had a lacunar infarct. Lacunar score was negative in 322 (82%) of patients without lacunar infarct. Patients with lacunar score had lower NIHSS (4 vs 7, p < 0.001), higher systolic (157 vs 151 mmHg, p = 0.001) and diastolic (86 vs 83 mmHg, p = 0.013) blood pressure and smaller infarct volume (2.4 vs 9.5 mL, p < 0.001). Performance of lacunar score was as follows: sensitivity 0.44; specificity 0.82; PPV 0.39; NPV 0.84; and accuracy 0.73. Assuming a prevalence of lacunar stroke of 13%, PPV lowered to 0.30 but NPV was 0.90. Lacunar score performed better for supratentorial lacunar infarcts. Lacunar score had a very good specificity and NPV for screening of lacunar stroke. Implementation of this simple tool into clinical practice may help hyperacute management and guide patient selection in clinical trials. Data supporting the results of this paper are available upon reasonable request to the corresponding author.

Cell-Based Therapies for the Treatment of Traumatic Brain Injury: Promises and Trajectories

Traumatic Brain Injury (TBI) is a debilitating condition that poses a significant public health concern. Historically linked to motor vehicle accidents, the epidemiology of TBI has evolved. Falls now emerge as the predominant cause, particularly among older adults. Sport-related TBIs have also garnered increased attention due to concerns regarding long-term neurological sequelae. To date, therapeutic interventions remain limited and have yet to yield substantial clinical benefits. Cell-based therapies offer promising avenues for neural repair and regeneration: endogenous stem cell therapies capitalize on endogenous pools that can be triggered by the injury and further enhanced by therapeutic approaches. In contrast, exogenous cell therapies provide an exogenous source of cells. However, challenges such as age-related decline in neurogenesis, age-related inflammation, and the heterogeneity of TBI present significant hurdles to overcome. Moreover, translating stem cell research from the laboratory to clinical applications necessitates the adherence to good manufacturing practice standards, which presents distinct obstacles. Addressing these challenges requires a multifaceted approach, including careful patient selection in clinical trials, appropriate experimental models, and the optimization of therapeutic techniques. Ultimately, a combination of strategies is likely to yield the most promising outcomes in the pursuit of effective TBI therapies.

Abstract 5136: Development of a highly specific NAPRT monoclonal antibody for the detection of tumors sensitive to pharmacological inhibition of NAMPT

Abstract Introduction: A key component of targeting synthetic lethality in cancer is identifying specific biomarkers, such as sources of NAD+ synthesis, that differentiate between healthy and diseased tissue. In mammals, NAD+ can be synthesized from nicotinamide via nicotinamide phosphoribosyltransferase (NAMPT) or from nicotinic acid (niacin, NA) via nicotinic acid phosphoribosyltransferase (NAPRT). NAPRT loss has been identified across tumor indications as a synthetic lethal sensitizer to small molecule inhibitors of NAMPT. Given the reliance of cancer cells on NAD+ synthesis, blocking NAD+ production is a compelling target for anti-cancer therapeutics. However, identifying tumors with NAPRT loss has been a challenge as NAPRT can be expressed by as many as 14 transcripts, only some of which code for the active enzyme. This complicates transcript-based analyses and necessitates proteomic assessment. Here, we have developed a mouse monoclonal antibody (4A5D7) to specifically detect active NAPRT and to identify indications enriched for NAPRT loss in patients through an IHC-based assay. We further confirm that loss of NAPRT makes cells susceptible to NAMPT inhibition, and ultimately show that our antibody can identify patient-derived xenograft (PDX) models that respond to NAMPT inhibition. Methods We utilized a hybridoma system to produce antibodies against epitopes in the NAPRT active site; clones were subsequently screened for their ability to selectively recognize active NAPRT. After identifying clone 4A5D7, we validated our proprietary antibody against FFPE specimens across multiple cancer indications (TNBC, NSCLC, SCLC, CRC, gastric, ovarian, and prostate). Stained samples were scored by a pathologist and assessed for percent of positive tumor cells, nuclear and/or cytoplasmic staining, and staining intensity. CCLE and TCGA datasets were mined for indications enriched for suspected NAPRT-loss and sensitivity to NAMPT inhibitors. We then selected cell lines and pan-tumor PDX tissue microarrays (TMAs) from indications of interest and characterized samples by western blot and IHC to determine NAPRT status. Cell lines and PDX tumor tissue were exposed to increasing ranges of NAMPTi in the presence or absence of NAPRT-substrate NA to validate our approach. Results: We validated that our proprietary NAPRT monoclonal antibody clone 4A5D7 only detects functional NAPRT and demonstrated improved performance over previous antibodies. We used our antibody to characterize cell lines and PDX tumor samples, and subsequently show that NAPRT-null cell lines and tumor samples are susceptible to NAMPT inhibition, while samples with functional NAPRT can be rescued by NA. Conclusion: Our proprietary antibody can identify NAPRT null tumors and identify samples that will respond to NAMPT inhibition, a critical advancement for patient selection in clinical trials. Citation Format: Josh Spurrier, Emmanuel S. Burgos, Mark R. Lundquist, Keiichiro Tanaka, Samuel Brook, Matt Gozzi, Jae-Sung Yi, Joseph Edmonds, Mitch Raponi. Development of a highly specific NAPRT monoclonal antibody for the detection of tumors sensitive to pharmacological inhibition of NAMPT [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5136.

Abstract 492: Investigating solute carrier SLC46A3 to enhance nanotherapeutic delivery for cancer

Abstract BACKGROUND: Nanoparticle therapeutics have multifaceted potential to improve efficacy and decrease toxicity of cancer treatments. Nanotherapeutics can encapsulate a range of cargos and have tunable surface chemistry that can enhance drug delivery to tumors. Cellular features also play a role in successful drug delivery, and predictive biomarkers may improve patient selection for clinical trials. In previous work, we identified SLC46A3, a poorly characterized lysosomal solute carrier, as a strong negative regulator of lipid-based nanoparticle uptake across many cancer models. We hypothesize that the expression of SLC46A3 will act as a predictive biomarker for therapeutic nanoparticles. METHODS: To test this hypothesis, we used two engineered model systems to probe the relationship between SLC46A3 expression and therapeutic response: lentiviral overexpression in a natively low-expressing melanoma model and genetic deactivation in a natively high-expressing breast cancer model. A library of FDA-approved and experimental therapeutic nanoparticles were screened in both models under a range of conditions. Liposomal formulations of chemotherapeutics were included with high and low surface functionalization of polyethylene glycol to investigate the impact of this ubiquitous ‘stealth’ polymer on our biomarker of interest. Solid lipid nanoparticles with nucleic acid cargo were also included. Orthogonal methods were used to probe these interactions and the therapeutic efficacy of nanotherapeutics, including pulsed dosing viability assays and label-free live-cell imaging. RESULTS: We found that SLC46A3 expression is a class-specific biomarker of lipid-based nanoparticle uptake and is predictive of distinctive patterns of response to therapy-loaded nanoparticles in both formulation and time-dependent manners. Additionally, we present a new technology for long-term, live cell imaging of nanoparticle uptake and trafficking that can discriminate mechanisms of cytotoxicity, accelerating the study of nano-bio interactions. These findings highlight a promising approach for the functional validation of nanoparticle-specific biomarkers using clinically relevant formulations that have the potential for rapid repurposing. Citation Format: Julianna Kenny-Serrano, Elizabeth B. Nelson, Gil Covarrubias, Namita Nabar, Joelle Straehla, Aanchal Gupta, Natalie Boehnke, Paula Hammond. Investigating solute carrier SLC46A3 to enhance nanotherapeutic delivery for cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 492.

Abstract 4718: Reclaiming a dirty drug: What is the context of vulnerability to arsenic trioxide in glioblastoma

Abstract Glioblastoma (GBM) is the most prevalent type of malignant tumor within the central nervous system. There is a growing demand to develop and adapt new therapeutic options in order to combat the five-year survival rate of 6.8% and improve patient quality of life. Arsenic trioxide (ATO) is the standard of care for myelodysplasia and relapsed or refractory acute promyelocytic leukemia. While currently being classified to have an idiopathic mechanism of action, two specific cytotoxic consequences of ATO have emerged: MNK1 activity and oxidative stress. Across six GBM PDX models a 20-fold difference in sensitivity manifests, indicative of underlying innate sensitivity or resistance to ATO. To correlate this to the proposed mechanisms of action, we observed ATO’s effect on MNK1 activity through eIF4E phosphorylation and glutathione (GSH) levels following treatment. A subset of GBM models exhibited a marked upregulation of eIF4E phosphorylation following treatment with 2 uM ATO. Of the six models, three were chosen to undergo further testing to observe whether this upregulation showed a consistent dose-dependent relationship across the models and correlated with vulnerability to ATO. Combination therapy with the MNK1 inhibitor, ETC-206, displayed mild to moderate synergy on overall cell viability and strong synergy when observing its effects on the glioma stem cell population. This preferential effect on the GSC populations suggests that MNK1i based combination strategies may show greater effectiveness in vivo than indicated by our previous in vitro studies. Nutraceuticals targeting GSH synthesis, Chrysin and Silibinin, displayed a 0.95 to 1.4-fold decrease in ATO IC50 values across all 6 models. To observe whether each nutraceutical impacted GSH levels consistently across all of the tested models, we measured GSH levels following treatment for each individual compound and their combination treatments. These findings further implicate MNK1 activity and cellular response to oxidative stress as markers of ATO sensitivity, however they are not the sole determinates of response. Understanding of the mechanism of action for idiopathic compounds may allow for the discovery of molecular signatures of sensitivity, improving patient selection for clinical trials and the development of new combination approaches to combat resistance in other tumor types. Citation Format: Charles Shaffer, Nanyun Tang, Yue Hao, Karen Fink, George Snipes, Bruce Mickey, Michael Berens. Reclaiming a dirty drug: What is the context of vulnerability to arsenic trioxide in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4718.

Advancing Rheumatology Care Through Machine Learning.

Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring.Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity.Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms.In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.

Immune Assessment Today: Optimizing and Standardizing Efforts to Monitor Immune Responses in Cancer and Beyond.

As part of a symposium, current and former directors of Immune Monitoring cores and investigative oncologists presented insights into the past, present and future of immune assessment. Dr. Gnjatic presented a classification of immune monitoring technologies ranging from universally applicable to experimental protocols, while emphasizing the need for assay harmonization. Dr. Obeng discussed physiologic differences among CD8 T cells that align with anti-tumor responses. Dr. Lyerly presented the Soldano Ferrone lecture, commemorating the passionate tumor immunologist who inspired many, and covered a timeline of monitoring technology development and its importance to immuno-oncology. Dr. Sonabend presented recent achievements in glioblastoma treatment, accentuating the range of monitoring techniques that allowed him to refine patient selection for clinical trials. Dr. Guevara-Patiño focused on hypoxia within the tumor environment and stressed that T cell viability is not to be confused with functionality. Dr. Butterfield accentuated monitoring of dendritic cell metabolic (dys)function as a determinant for tumor vaccine success. Lectures were interspersed with select abstract presentations. To summarize the concepts, Dr. Maecker from Stanford led an informative forum discussion, pointing towards the future of immune monitoring. Immune monitoring continues to be a guiding light towards effective immunotherapeutic strategies.

Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm

ObjectivesCancer cachexia is a debilitating condition with widespread negative effects. The heterogeneity of clinical features within patients with cancer cachexia is unclear. The identification and prognostic analysis of diverse phenotypes of cancer cachexia may help develop individualized interventions to improve outcomes for vulnerable populations. The aim of this study was to show that the machine learning–based cancer cachexia classification model generalized well on the external validation cohort. MethodsThis was a nationwide multicenter observational study conducted from October 2012 to April 2021 in China. Unsupervised consensus clustering analysis was applied based on demographic, anthropometric, nutritional, oncological, and quality-of-life data. Key characteristics of each cluster were identified using the standardized mean difference. We used logistic and Cox regression analysis to evaluate 1-, 3-, 5-y, and overall mortality. ResultsA consensus clustering algorithm was performed for 4329 patients with cancer cachexia in the discovery cohort, and four clusters with distinct phenotypes were uncovered. From clusters 1 to 4, the clinical characteristics of patients showed a transition from almost unimpaired to mildly, moderately, and severely impaired. Consistently, an increase in mortality from clusters 1 to 4 was observed. The overall mortality rate was 32%, 40%, 54%, and 68%, and the median overall survival time was 21.9, 18, 16.7, and 13.6 mo for patients in clusters 1 to 4, respectively. Our machine learning-based model performed better in predicting mortality than the traditional model. External validation confirmed the above results. ConclusionsMachine learning is valuable for phenotype classifications of patients with cancer cachexia. Detection of clinically distinct clusters among cachexic patients assists in scheduling personalized treatment strategies and in patient selection for clinical trials.

Cerebral small vessel disease modifies outcomes after minimally invasive surgery for intracerebral haemorrhage

BackgroundMinimally invasive surgery (MIS) for spontaneous supratentorial intracerebral haemorrhage (ICH) is controversial but may be beneficial if end-of-treatment (EOT) haematoma volume is reduced to ≤15 mL. We explored whether MRI...

Patterns and natural history of hand pain in individuals with symptomatic hand osteoarthritis in a prospective cohort study: A post-hoc analysis of a randomised controlled trial

ObjectiveTo investigate the variation in the distribution and the natural history of hand pain over 6 weeks in individuals with symptomatic hand osteoarthritis. DesignPatient-reported outcome data were collected at baseline and weekly for 6 weeks from community-based participants enrolled in a randomised controlled trial. Participants were grouped based on location of significant pain (Visual Analogue Scale, VAS≥40/100 ​mm) (both carpometacarpal (CMC) and interphalangeal (IP), CMC only, and IP only). ResultsAt baseline, of the 106 participants, 55(51.9 %) had pain in both CMC and IP joints, 28(26.4 %) in IP joints only, and 16(15.1 %) in CMC joint only. Those with CMC and IP pain had significantly higher VAS pain [68.1 (2.6) vs 59.3 (3.5) vs 51.2 (4.7)]; Australian Canadian Osteoarthritis Hand Index, (AUSCAN) pain [290.1 (15.7) vs 225.3 (21.2) vs 237.9 (28.4)], stiffness [57.1 (3.7) vs 44.6 (5.0) vs 32.2 (6.7)] and functional limitation [527.5 (30.9) vs 356.0 (41.7) vs 433.3 (55.7)]; and pain sensitization [PainDETECT score 11.1 (1.1) vs 8.1 (1.8) vs 5.8 (1.9)] compared to those with IP or CMC only pain, respectively. All groups showed improvement in outcomes over 6 weeks without significant inter-group differences. ConclusionIn a population with significant hand pain, pain in both CMC and IP joints was most common and identified a more severe phenotype than pain in IP or CMC only with higher pain, more functional limitation and pain sensitization. These data have the potential to inform clinical management of patients with hand pain and patient selection in clinical trials.

B-025 Evaluation of Bias Between Alzheimer’s Disease Blood-Based Biomarkers Assays and Their Concordance With Amyloid-PET on the Fujirebio Lumipulse and Quanterix Simoa Platforms

Abstract Background Blood-based biomarkers (BBBs) are a growing area of study for improving Alzheimer’s disease (AD) clinical evaluation. BBBs have been demonstrated to correlate with AD CSF biomarkers as well as neuroimaging results for assessment of amyloid pathology. Currently BBBs may be used as a screening tool for patient selection in clinical trials, and alongside more well characterized biomarkers like amyloid-PET and CSF markers for evaluation of individuals presenting with cognitive impairment. A comparative study of several AD BBBs assays was performed to assess bias between two platforms, as well as correlation to markers of amyloid pathology. Methods The study included 146 plasma samples from 112 cognitively normal (CN), and 34 mild cognitive impairment (MCI) or AD dementia individuals. Plasma Aβ42, Aβ40, and pTau181 concentrations were obtained using the Fujirebio Lumipulse and Quanterix Simoa immunoassays. Assay comparison between Fujirebio and Quanterix were performed for Aβ42, Aβ40, Aβ42/Aβ40 ratio, pTau181, and pTau181/Aβ42 ratio using Passing-Bablok regression and regression slopes were obtained. Amyloid-PET was performed using Pittsburg Compound B, and a standard uptake value ratio (SUVR) of 1.48 was used to classify amyloid status. In the CN group, 69 were amyloid negative, and 43 were amyloid positive. In the MCI/AD dementia group, 8 were amyloid negative and 26 were amyloid positive. Spearman’s rank correlation coefficient (rho) was used to determine correlation between assays and correlation of each with Amyloid-PET SUVRs. Results Between Fujirebio (y) and Quanterix (x) assays, measured Aβ42 and Aβ40 concentrations showed a large bias with y = 5.23x − 14.25, and y = 4.42x − 264.87, respectively. A moderate to strong correlation was observed between assays for Aβ42 and Aβ40 with rho values of 0.641 (P &amp;lt; 0.0001), and 0.498 (P &amp;lt; 0.0001), respectively. The ratio of Aβ42/Aβ40 corrected this bias with y = 0.96x + 0.03 and rho = 0.509 (P &amp;lt; 0.0001). pTau181 showed little bias between methods with y = 0.92x + 0.48, and a rho = 0.592 (P &amp;lt; 0.0001). However, the pTau181/Aβ42 ratio showed a large bias driven mostly by differences in the Aβ42 concentrations with y = 0.27x + 0.04 and rho = 0.574 (P &amp;lt; 0.0001). Quanterix assay results correlated with amyloid-PET SUVR yielded rho coefficients of AB42/40 ratio = −0.219 (P = 0.079), pTau181 = 0.380 (P &amp;lt; 0.0001), and pTau181/Aβ42 ratio = 0.466 (P &amp;lt; 0.0001). Fujirebio assay results correlated with amyloid-PET SUVR showed AB42/40 ratio rho = −0.551 (P &amp;lt; 0.0001), pTau181 rho = 0.370 (P &amp;lt; 0.0001), and pTau181/Aβ42 ratio rho = 0.391 (P &amp;lt; 0.0001). Conclusion This study demonstrates that some BBBs methods differ significantly between manufacturers. Little bias was observed between the Aβ42/Aβ40 and pTau181 determinations for the evaluated platforms; however, significant bias was observed in Aβ42, Aβ40, and the pTau181/Aβ42 ratio. The Fujirebio platform demonstrated stronger correlation of the Aβ42/Aβ40 ratio with amyloid-PET SUVR, while Quanterix showed better correlation of the pTau181/Aβ42 ratio. The pTau181 assays showed similar correlation to amyloid-PET SUVR. Bias between assays as well as clinical correlation differences may preclude the generalization of clinical performance of AD BBBs assays between platforms.

Early prediction of progression to Alzheimer’s disease using multi-modality neuroimages by a novel ordinal learning model ADPacer

Machine learning has shown great promise for integrating multi-modality neuroimaging datasets to predict the risk of progression/conversion to Alzheimer’s Disease (AD) for individuals with Mild Cognitive Impairment (MCI). Most existing work aims to classify MCI patients into converters versus non-converters using a pre-defined timeframe. The limitation is a lack of granularity in differentiating MCI patients who convert at different paces. Progression pace prediction has important clinical values, which allow from more personalized interventional strategies, better preparation of patients and their caregivers, and facilitation of patient selection in clinical trials. We proposed a novel ADPacer model which formulated the pace prediction into an ordinal learning problem with a unique capability of leveraging training samples with label ambiguity to augment the training set. This capability differentiates ADPacer from existing ordinal learning algorithms. We applied ADPacer to MCI patient cohorts from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL), and demonstrated the superior performance of ADPacer compared to existing ordinal learning algorithms. We also integrated the SHapley Additive exPlanations (SHAP) method with ADPacer to assess the contributions from different modalities to the model prediction. The findings are consistent with the AD literature.

Estimation of brain amyloid accumulation using deep learning in clinical [11C]PiB PET imaging

IntroductionEstimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images.MethodsA total of 1309 patients with cognitive impairment scanned with [11C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation.ResultsThe prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader’s classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R2 = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status.ConclusionThe proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.

Hypoxia-Inducible Factor-1α (HIF-1α) as a Biomarker for Changes in Microcirculation in Individuals with Systemic Sclerosis.

Systemic sclerosis is an autoimmune disease characterized by tissue fibrosis and microangiopathy. Vascular changes such as a decrease in capillary density diminish blood flow and impair tissue oxygenation. Reliable ways to monitor disease activity and predict disease progression are desired in the process of patient selection for clinical trials and to optimize individual patient outcomes. Hypoxia-inducible factor-1 (HIF-1) is a dimeric protein complex that plays an integral role in the body's response to hypoxia. Our study aimed to investigate the potential abnormalities of HIF-1α plasma concentration and its possible association with disease activity and vascular abnormalities in patients with systemic sclerosis. Blood plasma levels of HIF-1α were measured in patients with systemic sclerosis (n = 50) and in healthy individuals (n = 30) using commercially available ELISA test kits. The results showed a marked increase in HIF-1α levels in patients with systemic sclerosis (3.042ng/ml [2.295-7.749]) compared to the control group (1.969ng/ml [1.531-2.903] p < 0.01). Patients with diffuse cutaneous SSc (2.803ng/ml, IQR 2.221-8.799) and limited cutaneous SSc (3.231ng/ml, IQR 2.566-5.502) exhibited elevated serum HIF-1α levels compared to the control group (p < 0.01). We found a notable increase in HIF-1α plasma concentration in patients with an "active" pattern (6.625ng/ml, IQR 2.488-11.480) compared to those with either an "early" pattern (2.739, IQR 2.165-3.282, p < 0.05) or a "late" pattern (2.983ng/ml, IQR 2.229-3.386, p < 0.05). Patients with no history of digital ulcers had significantly higher levels of HIF-1α (4.367ng/ml, IQR 2.488-9.462) compared to patients with either active digital ulcers (2.832ng/ml, IQR 2.630-3.094, p < 0.05) or healed digital ulcers (2.668ng/ml, IQR 2.074-2.983, p < 0.05). Our results indicate that HIF-1α may serve as a biomarker in assessing microcirculatory changes in individuals with systemic sclerosis.

Clinical trials in dementia with Lewy bodies: the evolving concept of co-pathologies, patient selection and biomarkers.

Currently, no disease modifying therapies (DMTs) have been approved for use in dementia with Lewy bodies (DLB). Clinical trials face difficulties due to the clinical and neuropathological heterogeneity of the condition with a diverse array of neuropathogenic mechanisms contributing to the clinical phenotype. The purpose of this review is to describe how recent advances in the development of biofluid biomarkers may be used in clinical trials to tackle some of these challenges. Biomarkers are essential both to support the accurate diagnosis of DLB and to delineate the influence of coexisting pathologies. Recent advances in the development of α-synuclein seeding amplification assays (SAA) allow accurate identification of α-synuclein from the prodromal stages in DLB. Additionally, validation of plasma phosphorylated tau assays in DLB is ongoing and offers an accessible biomarker to indicate the existence of AD co-pathology. Use of biomarkers for diagnosis and group stratification in clinical trials of DLB is growing and likely to be of increasing importance in the future. In vivo biomarkers can enhance patient selection in clinical trials allowing greater diagnostic accuracy, a more homogeneous trial population, and stratification by co-pathology to create subgroups most likely to derive therapeutic benefit from DMTs.

Hepatocyte-derived biomarkers predict liver-related events at 2 years in Child-Pugh class A alcohol-related cirrhosis

We measured plasma keratin-18 and hepatocyte lEVs concentrations in 500 patients with Child-Pugh class A alcohol-related cirrhosis. Ability of these hepatocyte-derived biomarkers, alone or combined with MELD and FibroTest, to predict liver-related events at 2 years was analyzed, taking into account the alcohol consumption at inclusion and during the follow-up. Keratin-18 and hepatocyte lEVs concentrations increased with alcohol consumption. In patients without active alcohol consumption at enrollment (n=419), keratin-18 concentration predicted liver-related events at 2 years, independently of FibroTest and MELD. Patients with both keratin-18 concentration >285 U/L and FibroTest >0.74 had a 24% cumulative incidence of liver-related events at 2 years, versus 5% to 14% in other groups of patients. Similar results were obtained when combining keratin-18 concentration >285 U/L with MELD >10. In patients with active alcohol consumption at enrollment (n=81), hepatocyte lEVs predicted liver-related events at 2 years, independently of FibroTest and MELD. Patients with both hepatocyte lEVs concentration >50 U/L and FibroTest>0.74 had a 62% cumulative incidence of liver-related events at 2 years, versus 8% to 13% in other groups of patients. Combining hepatocyte lEVs concentration >50 U/L with MELD >10 had a lower discrimination ability. Similar results were obtained using as endpoint decompensation of cirrhosis defined according Baveno VII criteria. In patients with Child-Pugh class A alcohol-related cirrhosis, combining hepatocyte-derived biomarkers with FibroTest or MELD score identifies patients at high-risk of liver-related events, and could be used for risk stratification and patient selection in clinical trials.

Prospective evaluation of the efficacy, safety, and optimal biomarker enrichment strategy for nangibotide, a TREM-1 inhibitor, in patients with septic shock (ASTONISH): a double-blind, randomised, controlled, phase 2b trial

Activation of the triggering receptor expressed on myeloid cells-1 (TREM-1) pathway is associated with septic shock outcomes. Data suggest that modulation of this pathway in patients with activated TREM-1 might improve survival. Soluble TREM-1 (sTREM-1), a potential mechanism-based biomarker, might facilitate enrichment of patient selection in clinical trials of nangibotide, a TREM-1 modulator. In this phase 2b trial, we aimed to confirm the hypothesis that TREM1 inhibition might improve outcomes in patients with septic shock. This double-blind, randomised, placebo-controlled, phase 2b trial assessed the efficacy and safety of two different doses of nangibotide compared with placebo, and aimed to identify the optimum treatment population, in patients across 42 hospitals with medical, surgical, or mixed intensive care units (ICUs) in seven countries. Non-COVID-19 patients (18-85 years) meeting the standard definition of septic shock, with documented or suspected infection (lung, abdominal, or urinary [in patients ≥65 years]), were eligible within 24 h of vasopressor initiation for the treatment of septic shock. Patients were randomly assigned in a 1:1:1 ratio to intravenous nangibotide 0·3 mg/kg per h (low-dose group), nangibotide 1·0 mg/kg per h (high-dose group), or matched placebo, using a computer-generated block randomisation scheme (block size 3). Patients and investigators were masked to treatment allocation. Patients were grouped according to sTREM-1 concentrations at baseline (established from sepsis observational studies and from phase 2a change to data) into high sTREM-1 (≥ 400 pg/mL). The primary outcome was the mean difference in total Sequential Organ Failure Assessment (SOFA) score from baseline to day 5 in the low-dose and high-dose groups compared with placebo, measured in the predefined high sTREM-1 (≥ 400 pg/mL) population and in the overall modified intention-to-treat population. Secondary endpoints included all-cause 28-day mortality, safety, pharmacokinetics, and evaluation of the relationship between TREM-1 activation and treatment response. This study is registered with EudraCT, 2018-004827-36, and Clinicaltrials.gov, NCT04055909. Between Nov 14, 2019, and April 11, 2022, of 402 patients screened, 355 were included in the main analysis (116 in the placebo group, 118 in the low-dose group, and 121 in the high-dose group). In the preliminary high sTREM-1 population (total 253 [71%] of 355; placebo 75 [65%] of 116; low-dose 90 [76%] of 118; high-dose 88 [73%] of 121), the mean difference in SOFA score from baseline to day 5 was 0·21 (95% CI -1·45 to 1·87, p=0·80) in the low-dose group and 1·39 (-0·28 to 3·06, p=0·104) in the high-dose group versus placebo. In the overall population, the difference in SOFA score from baseline to day 5 between the placebo group and low-dose group was 0·20 (-1·09 to 1·50; p=0·76),and between the placebo group and the high-dose group was 1·06 (-0·23 to 2·35, p=0·108). In the predefined high sTREM-1 cutoff population, 23 (31%) patients in the placebo group, 35 (39%) in the low-dose group, and 25 (28%) in the high-dose group had died by day 28. In the overall population, 29 (25%) patients in the placebo, 38 (32%) in the low-dose, and 30 (25%) in the high-dose group had died by day 28. The number of treatment-emergent adverse events (111 [96%] patients in the placebo group, 113 [96%] in the low-dose group, and 115 [95%] in the high-dose group) and serious treatment-emergent adverse events (28 [24%], 26 [22%], and 31 [26%]) was similar between all three groups. High-dose nangibotide led to a clinically relevant improvement in SOFA score (of two points or more) from baseline to day 5 over placebo in those with higher cutoff concentrations (≥532 pg/mL) of sTREM-1 at baseline. Low dose nangibotide displayed a similar pattern with lower magnitude of effect across all cutoff values. This trial did not achieve the primary outcome of improvement in SOFA score at the predefined sTREM-1 value. Future studies are needed to confirm the benefit of nangibotide at higher concentrations of TREM-1 activation. Inotrem.

Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury.

Posttraumatic epilepsy (PTE) develops in as many as one third of severe traumatic brain injury (TBI) patients, often years after injury. Analysis of early electroencephalographic (EEG) features, by both standardized visual interpretation (viEEG) and quantitative EEG (qEEG) analysis, may aid early identification of patients at high risk for PTE. We performed a case-control study using a prospective database of severe TBI patients treated at a single center from 2011 to 2018. We identified patients who survived 2 years postinjury and matched patients with PTE to those without using age and admission Glasgow Coma Scale score. A neuropsychologist recorded outcomes at 1 year using the Expanded Glasgow Outcomes Scale (GOSE). All patients underwent continuous EEG for 3-5 days. A board-certified epileptologist, blinded to outcomes, described viEEG features using standardized descriptions. We extracted 14 qEEG features from an early 5-min epoch, described them using qualitative statistics, then developed two multivariable models to predict long-term risk of PTE (random forest and logistic regression). We identified 27 patients with and 35 without PTE. GOSE scores were similar at 1 year (p = .93). The median time to onset of PTE was 7.2 months posttrauma (interquartile range = 2.2-22.2 months). None of the viEEG features was different between the groups. On qEEG, the PTE cohort had higher spectral power in the delta frequencies, more power variance in the delta and theta frequencies, and higher peak envelope (all p < .01). Using random forest, combining qEEG and clinical features produced an area under the curve of .76. Using logistic regression, increases in the delta:theta power ratio (odds ratio [OR] = 1.3, p < .01) and peak envelope (OR = 1.1, p < .01) predicted risk for PTE. In a cohort of severe TBI patients, acute phase EEG features may predict PTE. Predictive models, as applied to this study, may help identify patients at high risk for PTE, assist early clinical management, and guide patient selection for clinical trials.

Development and validation of a unifying pre-treatment decision tool for intracranial and extracranial metastasis-directed radiotherapy.

Though metastasis-directed therapy (MDT) has the potential to improve overall survival (OS), appropriate patient selection remains challenging. We aimed to develop a model predictive of OS to refine patient selection for clinical trials and MDT. We assembled a multi-institutional cohort of patients treated with MDT (stereotactic body radiation therapy, radiosurgery, and whole brain radiation therapy). Candidate variables for recursive partitioning analysis were selected per prior studies: ECOG performance status, time from primary diagnosis, number of additional non-target organ systems involved (NOS), and intracranial metastases. A database of 1,362 patients was assembled with 424 intracranial, 352 lung, and 607 spinal treatments (n=1,383). Treatments were split into training (TC) (70%, n=968) and internal validation (IVC) (30%, n=415) cohorts. The TC had median ECOG of 0 (interquartile range [IQR]: 0-1), NOS of 1 (IQR: 0-1), and OS of 18 months (IQR: 7-35). The resulting model components and weights were: ECOG = 0, 1, and > 1 (0, 1, and 2); 0, 1, and > 1 NOS (0, 1, and 2); and intracranial target (2), with lower scores indicating more favorable OS. The model demonstrated high concordance in the TC (0.72) and IVC (0.72). The score also demonstrated high concordance for each target site (spine, brain, and lung). This pre-treatment decision tool represents a unifying model for both intracranial and extracranial disease and identifies patients with the longest survival after MDT who may benefit most from aggressive local therapy. Carefully selected patients may benefit from MDT even in the presence of intracranial disease, and this model may help guide patient selection for MDT.